Image classification serves as a essential task of medical image processing, relying heavily on labeled data. Due to the necessity of professional medical knowledge, it is difficult to rapidly accumulate a large number of annotations through crowd-sourcing. Research on active learning has been developed to select the most valuable data within limited labeling budgets. Traditional active learning methods select samples by uncertainty measure or representative information. They may overlook the distribution characteristics and boundary information of samples in the feature space. We introduce a novel active learning approach called Active Learning by Feature Perturbation (ALFP). It evaluates samples based on the impact of feature perturbation on model decisions and the degree of perturbations. ALFP first assigns mandatory labels of each class to guide perturbations and obtain new predictions of samples. If the model prediction of sample changes, we regard it as an uncertain one. Then the perturbation distances obtained by feature perturbators are regarded as diversity vectors, which can be used to select valuable samples by clustering. Finally, we select the data points that are closest to the cluster centers in the cluster space with the same number of centers. In this paper, we conduct experiments using traditional active learning settings on classification tasks of three public medical image datasets. We compare our method with several representative active learning approaches and our ALFP obtains the state-of-the-art (SOTA) performance. It demonstrates that perturbation-based information can effectively evaluate sample value and enhance sampling strategies for active learning.