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Medical image segmentation has immense clinical applicability but remains achallenge despite advancements in deep learning. The Segment Anything Model(SAM) exhibits potential in this field, yet the requirement for expertiseintervention and the domain gap between natural and medical images posessignificant obstacles. This paper introduces a novel training-free evidentialprompt generation method named EviPrompt to overcome these issues. The proposedmethod, built on the inherent similarities within medical images, requires onlya single reference image-annotation pair, making it a training-free solutionthat significantly reduces the need for extensive labeling and computationalresources. First, to automatically generate prompts for SAM in medical images,we introduce an evidential method based on uncertainty estimation without theinteraction of clinical experts. Then, we incorporate the human prior into theprompts, which is vital for alleviating the domain gap between natural andmedical images and enhancing the applicability and usefulness of SAM in medicalscenarios. EviPrompt represents an efficient and robust approach to medicalimage segmentation, with evaluations across a broad range of tasks andmodalities confirming its efficacy.

EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images