Title: SAAL: Sharpness-Aware Active Learning. Abstract: While modern deep neural networks play significant roles in many research areas, they are also prone to overfitting problems under limited data instances. Particularly, this overfitting, or generalization issue, could be a problem in the framework of active learning because it selects a few data instances for learning over time. To consider the generalization, this paper introduces the first active learning method to incorporate the sharpness of loss space in the design of the acquisition function, inspired by sharpness-aware minimization (SAM). SAM intends to maximally perturb the training dataset, so the optimization can be led to a flat minima, which is known to have better generalization ability. Specifically, our active learning, Sharpness-Aware Active Learning (SAAL), constructs its acquisition function by selecting unlabeled instances whose perturbed loss becomes maximum. Over the adaptation of SAM into SAAL, we design a pseudo labeling mechanism to look forward to the perturbed loss w.r.t. the ground-truth label. Furthermore, we present a theoretic analysis between SAAL and recent active learning methods, so the recent works could be reduced to SAAL under a specific condition. We conduct experiments on various benchmark datasets for vision-based tasks in image classification and object detection. The experimental results confirm that SAAL outperforms the baselines by selecting instances that have the potentially maximal perturbation on the loss.