diff --git a/docs/source/polars.md b/docs/source/polars.md
index 584cb15c..7f832fa0 100644
--- a/docs/source/polars.md
+++ b/docs/source/polars.md
@@ -532,7 +532,11 @@ from pandera.engines.polars_engine import DateTime
 
 
 class DateTimeModel(pa.DataFrameModel):
-    created_at: Annotated[DateTime, True]
+    created_at: Annotated[DateTime, True, "us", None]
+```
+.
+```{note}
+For `Annotated` types, you need to pass in all positional and keyword arguments.
 ```
 
 :::
diff --git a/pandera/api/polars/model.py b/pandera/api/polars/model.py
index ca8b0eb5..25608dff 100644
--- a/pandera/api/polars/model.py
+++ b/pandera/api/polars/model.py
@@ -1,5 +1,6 @@
 """Class-based api for polars models."""
 
+import inspect
 from typing import Dict, List, Tuple, Type
 
 import pandas as pd
@@ -47,10 +48,16 @@ def _build_columns(  # pylint:disable=too-many-locals
             field_name = field.name
             check_name = getattr(field, "check_name", None)
 
-            engine_dtype = None
             try:
                 engine_dtype = pe.Engine.dtype(annotation.raw_annotation)
-                dtype = engine_dtype.type
+                if inspect.isclass(annotation.raw_annotation) and issubclass(
+                    annotation.raw_annotation, pe.DataType
+                ):
+                    # use the raw annotation as the dtype if it's a native
+                    # pandera polars datatype
+                    dtype = annotation.raw_annotation
+                else:
+                    dtype = engine_dtype.type
             except (TypeError, ValueError) as exc:
                 if annotation.metadata:
                     if field.dtype_kwargs:
@@ -64,13 +71,13 @@ def _build_columns(  # pylint:disable=too-many-locals
                 elif annotation.default_dtype:
                     dtype = annotation.default_dtype
                 else:
-                    dtype = annotation.arg
+                    dtype = annotation.arg  # type: ignore
 
             if (
                 annotation.origin is None
                 or isinstance(annotation.origin, pl.datatypes.DataTypeClass)
                 or annotation.origin is Series
-                or engine_dtype
+                or dtype
             ):
                 if check_name is False:
                     raise SchemaInitError(
diff --git a/tests/polars/test_polars_model.py b/tests/polars/test_polars_model.py
index d420b528..b88481f1 100644
--- a/tests/polars/test_polars_model.py
+++ b/tests/polars/test_polars_model.py
@@ -3,9 +3,18 @@
 import sys
 from typing import Optional
 
+try:  # python 3.9+
+    from typing import Annotated  # type: ignore
+except ImportError:
+    from typing_extensions import Annotated  # type: ignore
+
 import polars as pl
 import pytest
+from hypothesis import given
+from hypothesis import strategies as st
+from polars.testing.parametric import column, dataframes
 
+import pandera.engines.polars_engine as pe
 from pandera.errors import SchemaError
 from pandera.polars import (
     Column,
@@ -211,3 +220,53 @@ class ModelWithNestedDtypes(DataFrameModel):
 
     schema = ModelWithNestedDtypes.to_schema()
     assert schema_with_list_type == schema
+
+
+@pytest.mark.parametrize(
+    "time_zone",
+    [
+        None,
+        "UTC",
+        "GMT",
+        "EST",
+    ],
+)
+@given(st.data())
+def test_dataframe_schema_with_tz_agnostic_dates(time_zone, data):
+    strategy = dataframes(
+        column("datetime_col", dtype=pl.Datetime()),
+        lazy=True,
+        size=10,
+    )
+    lf = data.draw(strategy)
+    lf = lf.cast({"datetime_col": pl.Datetime(time_zone=time_zone)})
+
+    class ModelTZAgnosticKwargs(DataFrameModel):
+        datetime_col: pe.DateTime = Field(
+            dtype_kwargs={"time_zone_agnostic": True}
+        )
+
+    class ModelTZSensitiveKwargs(DataFrameModel):
+        datetime_col: pe.DateTime = Field(
+            dtype_kwargs={"time_zone_agnostic": False}
+        )
+
+    class ModelTZAgnosticAnnotated(DataFrameModel):
+        datetime_col: Annotated[pe.DateTime, True, "us", None]
+
+    class ModelTZSensitiveAnnotated(DataFrameModel):
+        datetime_col: Annotated[pe.DateTime, False, "us", None]
+
+    for tz_agnostic_model in (
+        ModelTZAgnosticKwargs,
+        ModelTZAgnosticAnnotated,
+    ):
+        tz_agnostic_model.validate(lf)
+
+    for tz_sensitive_model in (
+        ModelTZSensitiveKwargs,
+        ModelTZSensitiveAnnotated,
+    ):
+        if time_zone:
+            with pytest.raises(SchemaError):
+                tz_sensitive_model.validate(lf)