Object detection based on active learning typically utilizes limited labeled data to enhance detection model performance. This method allows learners to select valuable samples from a large pool of unlabeled data for manual labeling and to iteratively train and optimize the model. However, existing object detection methods that use active learning often struggle to effectively balance sample uncertainty and diversity, which results in high redundancy of query samples. To address this issue, we propose an adversarial active learning method guided by uncertainty for object detection. First, we introduce a loss prediction module to evaluate the uncertainty of unlabeled samples. This uncertainty guides the adversarial network training and helps construct a query sample set that includes both uncertainty and diversity. Second, we evaluate sample diversity based on feature similarity to reduce redundancy of query samples. Finally, experimental results on the MS COCO and Pascal VOC datasets using multiple detection frameworks demonstrate that the proposed method can effectively improve object detection accuracy with fewer annotations.