  Real-world medical image segmentation has tremendous long-tailed complexity
of objects, among which tail conditions correlate with relatively rare diseases
and are clinically significant. A trustworthy medical AI algorithm should
demonstrate its effectiveness on tail conditions to avoid clinically dangerous
damage in these out-of-distribution (OOD) cases. In this paper, we adopt the
concept of object queries in Mask Transformers to formulate semantic
segmentation as a soft cluster assignment. The queries fit the feature-level
cluster centers of inliers during training. Therefore, when performing
inference on a medical image in real-world scenarios, the similarity between
pixels and the queries detects and localizes OOD regions. We term this OOD
localization as MaxQuery. Furthermore, the foregrounds of real-world medical
images, whether OOD objects or inliers, are lesions. The difference between
them is less than that between the foreground and background, possibly
misleading the object queries to focus redundantly on the background. Thus, we
propose a query-distribution (QD) loss to enforce clear boundaries between
segmentation targets and other regions at the query level, improving the inlier
segmentation and OOD indication. Our proposed framework is tested on two
real-world segmentation tasks, i.e., segmentation of pancreatic and liver
tumors, outperforming previous state-of-the-art algorithms by an average of
7.39% on AUROC, 14.69% on AUPR, and 13.79% on FPR95 for OOD localization. On
the other hand, our framework improves the performance of inlier segmentation
by an average of 5.27% DSC when compared with the leading baseline nnUNet.
