  Uncertainty quantification (UQ) has become critical for evaluating the
reliability of artificial intelligence systems, especially in medical image
segmentation. This study addresses the interpretability of instance-wise
uncertainty values in deep learning models for focal lesion segmentation in
magnetic resonance imaging, specifically cortical lesion (CL) segmentation in
multiple sclerosis. CL segmentation presents several challenges, including the
complexity of manual segmentation, high variability in annotation, data
scarcity, and class imbalance, all of which contribute to aleatoric and
epistemic uncertainty. We explore how UQ can be used not only to assess
prediction reliability but also to provide insights into model behavior, detect
biases, and verify the accuracy of UQ methods. Our research demonstrates the
potential of instance-wise uncertainty values to offer post hoc global model
explanations, serving as a sanity check for the model. The implementation is
available at https://github.com/NataliiaMolch/interpret-lesion-unc.
