In lots of important applications, such as malignant cell detection, network intrusion detection, error signal detection in power system, the data distributions of positive and negative classes are usually imbalance. Many classifiers could not perform well in data imbalance cases. The major problem is that classifiers tend to ignore samples and accuracy of the minority class without regarding the higher cost of misclassification in this minor class. Therefore, pattern classification for imbalance data becomes a hot challenge to both academy and industry. In this paper, we propose an active learning method for imbalance data using a stochastic sensitivity measure (ST-SM) of Radial Basis Function Neural Network (RBFNN). A large ST-SM indicates the RBFNN is uncertain and yields a large output fluctuation around a particular sample. These samples yielding large ST-SM values are selected for adding to the training set in each turn. Empirically, samples with large output perturbation (i.e. large ST-SM) should be located near the classification boundary and is of great significance for the training of classifier. As for the imbalance characteristic of the data set, the ST-SM should be able to reduce the number of redundant samples being selected in the majority class, rebalance the sample distribution of the training set, and finally improve the performance of the classifier.