In order to solve the problem that selected accuracy stopping criterion can only be applied in the scenario of batch mode-based active learning, an improved stopping criterion for single-labeling mode was proposed. The matching relationship between each predicted label and the corresponding real label existing in a pre-designed number of learning rounds was used to approximately estimate and calculate the selected accuracy. The higher the match quality was, the higher the selected accuracy was. Then, the variety of selected accuracy could be monitored by moving a sliding-time window. Active learning would stop when the selected accuracy was higher than a pre-designed threshold. The experiments were conducted on 6 baseline data sets with active learning algorithm based on Support Vector Machine (SVM) classifier for indicating the effectiveness and feasibility of the proposed criterion. The experimental results show that when pre-designing an appropriate threshold, active learning can stop at the right time. The proposed method expands the applications of selected accuracy stopping criterion and improves its practicability.