In the semi-supervised object detection task, due to the scarcity of labeled data and the diversity and complexity of objects to be detected, the quality of pseudo-labels generated by existing methods for unlabeled data is relatively low, which severely restricts the performance of semi-supervised object detection. In this paper, we revisit the pseudo-labeling based Teacher-Student mutual learning framework for semi-supervised object detection and identify that the inconsistency of the location and feature of the candidate object proposals between the Teacher and the Student branches are the fatal cause of the low quality of the pseudo labels. To address this issue, we propose a simple yet effective technique within the mainstream teacher-student framework, called Double Check Soft Teacher, to overcome the harm caused by insufficient quality of pseudo labels. Specifically, our proposed method leverages teacher model to generate pseudo labels for the student model. Especially, the candidate boxes generated by the student model based on the pseudo label will be sent to the teacher model for "double check", and then the teacher model will output probabilistic soft label with background class for those candidate boxes, which will be used to train the student model. Together with a pseudo labeling mechanism based on the sum of the TOP-K prediction score, which improves the recall rate of pseudo labels, Double Check Soft Teacher consistently surpasses state-of-the-art methods by significant margins on the MS-COCO benchmark, pushing the new state-of-the-art. Source codes are available at https://github.com/wkfdb/DCST.