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We empirically evaluate a distance-guided learning method embedded in a multiple classifier system (MCS) for tissue segmentation in optical images of the uterine cervix. Instead of combining multiple base classifiers as in traditional ensemble methods, we propose a Bhattacharyya distance based metric for measuring the similarity in decision boundary shapes between a pair of statistical classifiers. By generating an ensemble of base classifiers trained independently on separate training images, we can use the distance metric to select those classifiers in the ensemble whose decision boundaries are similar to that of an unknown test image. In an extreme case, we select the base classifier with the most similar decision boundary to accomplish classification and segmentation on the test image. Our approach is novel in the way that the nearest neighbor is picked and effectively solves classification problems in which base classifiers with good overall performance are not easy to construct due to a large variation in the training examples. In our experiments, we applied our method and several popular ensemble methods to segmenting acetowhite regions in cervical images. The overall classification accuracy of the proposed method is significantly better than that of a single classifier learned using the entire training set, and is also superior to other ensemble methods including majority voting, STAPLE, Boosting and Bagging.