  Shortcut learning is a phenomenon where machine learning models prioritize
learning simple, potentially misleading cues from data that do not generalize
well beyond the training set. While existing research primarily investigates
this in the realm of image classification, this study extends the exploration
of shortcut learning into medical image segmentation. We demonstrate that
clinical annotations such as calipers, and the combination of zero-padded
convolutions and center-cropped training sets in the dataset can inadvertently
serve as shortcuts, impacting segmentation accuracy. We identify and evaluate
the shortcut learning on two different but common medical image segmentation
tasks. In addition, we suggest strategies to mitigate the influence of shortcut
learning and improve the generalizability of the segmentation models. By
uncovering the presence and implications of shortcuts in medical image
segmentation, we provide insights and methodologies for evaluating and
overcoming this pervasive challenge and call for attention in the community for
shortcuts in segmentation. Our code is public at
https://github.com/nina-weng/shortcut_skinseg .
