An Efficient Multi-Rater Setup Towards Personalized and Diversified Medical Image Segmentation

Multi-rater medical image segmentation addresses annotation ambiguities but typically requires costly multiple expert annotations per scan. We propose P-Diverse, a novel two-stage framework that minimizes the annotation needs while achieving state-of-the-art performance. Stage-I trains a modified nnU-Net with expert-specific embeddings throughout the network stages, generating personalized segmentations using as low as one annotation per scan. Stage-II freezes that network to synthesize the missing annotations and trains a diversification model that captures multi-rater variability using the available and synthesized annotations. We evaluated on the public NPC dataset and QUBIQ2021 dataset (where the current SOTA method fails), P-Diverse establishes new SOTA performance using synthetic annotations on the diversification stage, significantly reducing clinical annotation burdens. Code: https://github.com/XXX/XXX.