+Our uncertainty-aware interactive segmentation model, <b>SPA</b>, efficiently achieves segmentations whose decisions on uncertain pixels are aligned with users preferences. This is achieved by modeling uncertainties and human interactions. At inference time, users are presented with one recommended prediction and a few representative segmentations that capture uncertainty, allowing users to select the one best aligned with their clinical needs. If the user is unsatisfied with the recommended prediction, the model learns from the users' selections, adapts itself, and presents users a new set of representative segmentations. Our approach minimizes user interactions and eliminates the need for painstaking pixel-wise adjustments compared to conventional interactive segmentation models.
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