Learning to Abstain: Reliable Medical Image Segmentation With Rejection Option

Abstract

Uncertainty estimation is an important area of research in the deep learning field by offering numerous potential applications. Selective prediction is one of the possible applications that focuses on rejecting less confident samples to enhance the overall performance and reliability of accepted samples. In the context of segmentation, selective segmentation aims to allow the model to abstain from assigning labels to uncertain pixels. While selective classification has received considerable attention, only a few studies have explored selective segmentation. In this paper, we introduce a novel approach that addresses this gap by incorporating uncertainty learning during model training. Specifically, we propose a new training scheme that encourages the model to identify and alarm uncertain pixels in a real-time manner. Comparative experiments on three public medical segmentation datasets demonstrate the effectiveness of our method. Our approach achieves higher Dice scores across multiple coverage levels, surpassing the performance of existing approaches in the field.