The field of medical image segmentation is moving towards incorporating anatomical context and uncertainty into deep learning models. Researchers are exploring ways to leverage existing anatomy segmentation models and integrate them into standard pathology optimization regimes. Another trend is the development of data augmentation frameworks that model anatomical continuity and focus on hard-to-segment regions. Semi-supervised learning methods are also being investigated, particularly in the context of tumor segmentation. Noteworthy papers include: GRASP, which introduces a modular plug-and-play framework for enhancing pathology segmentation models by leveraging existing anatomy segmentation models. JanusNet, which proposes a data augmentation framework for 3D medical data that globally models anatomical continuity while locally focusing on hard-to-segment regions. IPA-CP, which introduces a straightforward yet effective approach for tumor segmentation in CT scans using iterative pseudo-labeling based adaptive copy-paste supervision.