SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation

Medical image segmentation data inherently contain uncertainty, often stemming from both imperfect image quality and variability in labeling preferences on ambiguous pixels, which depend on annotators' expertise and the clinical context of the annotations. For instance, a boundary pixel might be labeled as tumor in diagnosis to avoid under-assessment of severity, but as normal tissue in radiotherapy to prevent damage to sensitive structures. As segmentation preferences vary across downstream applications, it is often desirable for an image segmentation model to offer user-adaptable predictions rather than a fixed output. While prior uncertainty-aware and interactive methods offer adaptability, they are inefficient at test time: uncertainty-aware models require users to choose from numerous similar outputs, while interactive models demand significant user input through click or box prompts to refine segmentation. To address these challenges, we propose \textbf{SPA}, a segmentation framework that efficiently adapts to diverse test-time preferences with minimal human interaction. By presenting users a select few, distinct segmentation candidates that best capture uncertainties, it reduces clinician workload in reaching the preferred segmentation. To accommodate user preference, we introduce a probabilistic mechanism that leverages user feedback to adapt model's segmentation preference. The proposed framework is evaluated on a diverse range of medical image segmentation tasks: color fundus images, CT, and MRI. It demonstrates 1) a significant reduction in clinician time and effort compared with existing interactive segmentation approaches, 2) strong adaptability based on human feedback, and 3) state-of-the-art image segmentation performance across diverse modalities and semantic labels.