Semi-supervised learning is a learning method that uses both labeled and unlabeled samples to improve the performance of the model while reducing labeling costs. When there were tens to hundreds of labeled samples, semi-supervised learning methods showed good performance, but most of them showed poor performance when only a small number of labeled samples were given. In this paper, we focus on challenging label-scarce environments, where there are only a few labeled samples per class. Our proposed model, ExMatch, is designed to obtain reliable information from unlabeled samples using self-supervised models and utilize it for semi-supervised learning. In the training process, ExMatch guides the model to maintain an appropriate distribution and resist learning from incorrect pseudo-labels based on the information from self-supervised models and its own model. ExMatch shows very stable training progress and the state-of-the-art performances on multiple benchmark datasets. In extremely label-scare situations, performances are improved by about 5% to 21% for CIFAR-10/100 and SVHN. ExMatch also demonstrates significant performance improvements in high-resolution and large-scale dataset such as STL-10, Tiny-ImageNet, and ImageNet.