Comparing with the baseline stopping criterion, a better stopping criterion not only achieves almost the same performance, but also has needed to learn fewer unlabeled examples when the active learning process is ended.
for a stopping criterion of active learning, the fewer unlabeled examples that have been leaned when it is met, the bigger reduction in human labeling cost is made.