Predictive Accuracy-Based Active Learning for Medical Image Segmentation

TL;DR

This paper introduces PAAL, a novel active learning method for medical image segmentation that predicts accuracy to select informative samples, significantly reducing annotation costs while maintaining high segmentation performance.

Contribution

The paper proposes a predictive accuracy-based active learning framework with an Accuracy Predictor and Weighted Polling Strategy, improving sample selection for medical image segmentation.

Findings

Achieves comparable accuracy to fully annotated data

Reduces annotation costs by 50% to 80%

Demonstrates superior performance on multiple datasets

Abstract

Active learning is considered a viable solution to alleviate the contradiction between the high dependency of deep learning-based segmentation methods on annotated data and the expensive pixel-level annotation cost of medical images. However, most existing methods suffer from unreliable uncertainty assessment and the struggle to balance diversity and informativeness, leading to poor performance in segmentation tasks. In response, we propose an efficient Predictive Accuracy-based Active Learning (PAAL) method for medical image segmentation, first introducing predictive accuracy to define uncertainty. Specifically, PAAL mainly consists of an Accuracy Predictor (AP) and a Weighted Polling Strategy (WPS). The former is an attached learnable module that can accurately predict the segmentation accuracy of unlabeled samples relative to the target model with the predicted posterior probability. The latter provides an efficient hybrid querying scheme by combining predicted accuracy and feature representation, aiming to ensure the uncertainty and diversity of the acquired samples. Extensive experiment results on multiple datasets demonstrate the superiority of PAAL. PAAL achieves comparable accuracy to fully annotated data while reducing annotation costs by approximately 50% to 80%, showcasing significant potential in clinical applications. The code is available at https://github.com/shijun18/PAAL-MedSeg.