diff --git a/scripts/datasets/medical/check_acouslic_ai.py b/scripts/datasets/medical/check_acouslic_ai.py
new file mode 100644
index 00000000..eb5f63d0
--- /dev/null
+++ b/scripts/datasets/medical/check_acouslic_ai.py
@@ -0,0 +1,24 @@
+from torch_em.data import MinInstanceSampler
+from torch_em.util.debug import check_loader
+from torch_em.data.datasets.medical import get_acouslic_ai_loader
+
+
+ROOT = "/media/anwai/ANWAI/data/acouslic_ai"
+
+
+def check_acouslic_ai():
+    loader = get_acouslic_ai_loader(
+        path=ROOT,
+        patch_shape=(1, 512, 512),
+        ndim=2,
+        batch_size=2,
+        resize_inputs=False,
+        download=False,
+        sampler=MinInstanceSampler()
+    )
+
+    check_loader(loader, 8)
+
+
+if __name__ == "__main__":
+    check_acouslic_ai()
diff --git a/torch_em/data/datasets/medical/__init__.py b/torch_em/data/datasets/medical/__init__.py
index 88c0ffa9..a12e1586 100644
--- a/torch_em/data/datasets/medical/__init__.py
+++ b/torch_em/data/datasets/medical/__init__.py
@@ -1,4 +1,5 @@
 from .autopet import get_autopet_loader
+from .acouslic_ai import get_acouslic_ai_dataset, get_acouslic_ai_loader
 from .btcv import get_btcv_dataset, get_btcv_loader
 from .busi import get_busi_dataset, get_busi_loader
 from .camus import get_camus_dataset, get_camus_loader
diff --git a/torch_em/data/datasets/medical/acouslic_ai.py b/torch_em/data/datasets/medical/acouslic_ai.py
new file mode 100644
index 00000000..41999610
--- /dev/null
+++ b/torch_em/data/datasets/medical/acouslic_ai.py
@@ -0,0 +1,87 @@
+import os
+from glob import glob
+from natsort import natsorted
+from typing import Tuple, Union
+
+import torch_em
+
+from .. import util
+
+
+URL = "https://zenodo.org/records/11005384/files/acouslic-ai-train-set.zip"
+CHECKSUM = "187602dd243a3a872502b57b8ea56e28c67a9ded547b6e816b00c6d41f8b8767"
+
+
+def get_acouslic_ai_data(path, download):
+    os.makedirs(path, exist_ok=True)
+
+    data_dir = os.path.join(path, "data")
+    if os.path.exists(data_dir):
+        return data_dir
+
+    zip_path = os.path.join(path, "acouslic-ai-train-set.zip")
+    util.download_source(path=zip_path, url=URL, download=download, checksum=CHECKSUM)
+    util.unzip(zip_path=zip_path, dst=data_dir, remove=False)
+
+    return data_dir
+
+
+def _get_acouslic_ai_paths(path, download):
+    data_dir = get_acouslic_ai_data(path=path, download=download)
+
+    image_paths = natsorted(glob(os.path.join(data_dir, "images", "stacked_fetal_ultrasound", "*.mha")))
+    gt_paths = natsorted(glob(os.path.join(data_dir, "masks", "stacked_fetal_abdomen", "*.mha")))
+
+    return image_paths, gt_paths
+
+
+def get_acouslic_ai_dataset(
+    path: Union[os.PathLike, str],
+    patch_shape: Tuple[int, ...],
+    resize_inputs: bool = False,
+    download: bool = False,
+    **kwargs
+):
+    """Dataset for segmentation of fetal abdominal circumference in ultrasound images.
+
+    This dataset is from the ACOUSLIC-AI Challenge: https://acouslic-ai.grand-challenge.org/
+
+    Please cite it if you this dataset for your publication.
+    """
+    image_paths, gt_paths = _get_acouslic_ai_paths(path=path, download=download)
+
+    if resize_inputs:
+        resize_kwargs = {"patch_shape": patch_shape, "is_rgb": False}
+        kwargs, patch_shape = util.update_kwargs_for_resize_trafo(
+            kwargs=kwargs, patch_shape=patch_shape, resize_inputs=resize_inputs, resize_kwargs=resize_kwargs
+        )
+
+    dataset = torch_em.default_segmentation_dataset(
+        raw_paths=image_paths,
+        raw_key=None,
+        label_paths=gt_paths,
+        label_key=None,
+        patch_shape=patch_shape,
+        **kwargs
+    )
+
+    return dataset
+
+
+def get_acouslic_ai_loader(
+    path: Union[os.PathLike, str],
+    patch_shape: Tuple[int, ...],
+    batch_size: int,
+    resize_inputs: bool = False,
+    download: bool = False,
+    **kwargs
+):
+    """Dataloader for segmentation of fetal abdominal circumference in ultrasound images.
+    See `get_acouslic_ai_dataset` for details.
+    """
+    ds_kwargs, loader_kwargs = util.split_kwargs(torch_em.default_segmentation_dataset, **kwargs)
+    dataset = get_acouslic_ai_dataset(
+        path=path, patch_shape=patch_shape, resize_inputs=resize_inputs, download=download, **ds_kwargs
+    )
+    loader = torch_em.get_data_loader(dataset=dataset, batch_size=batch_size, **loader_kwargs)
+    return loader