  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.
