Medical image segmentation plays a critical role in both image interpretation and disease diagnosis. Data-driven approaches such as U-Net have significantly advanced the field by enabling pixel-level classification of anatomical structures. However, the resulting segmentation masks often fail to comply with spatial anatomical constraints. Although properties such as organ connectedness and relative position are well understood by experts, current segmentation practices lack a systematic and explicit means to express, incorporate, or monitor this domain knowledge. In this work, we conduct a systematic study of formal specification rules across diverse medical imaging tasks with domain experts. Based on our analysis, we introduce Image Segmentation Logic (Isl), a novel spatial formalism designed to bridge the gap between expert knowledge and automated analysis. This logic provides an expressive and interpretable framework to specify critical domain knowledge and spatial constraints, and to automatically monitor their satisfaction with segmentation outputs. It integrates variables such as intensity, predicted class labels, confidence scores, and directional relationships, enabling both pixel-level and region-level specification checking. To support practical adoption, we develop an efficient monitoring tool for evaluating segmentation outputs against Isl specifications and formally prove the soundness and correctness of Isl’s semantics. We demonstrate the expressiveness and utility of Isl through two case studies on established medical image segmentation datasets. Our results show that Isl enables precise detection of segmentation violations and provides more fine-grained validation than traditional metrics.