Carotid plaques in ultrasound images are a routine indicator for stroke accident risk evaluation. However, plaque segmentation for diagnosis is a difficult task because artifacts and heterogeneity can obfuscate the plaque boundaries. Moreover, pixel-level labeling of numerous images can be time-consuming and laborious. In this paper, we propose a discriminative consistency semi-supervised method by employing global contexts, named DCGC-Net, to segment carotid ultrasound plaques. Firstly, student-teacher consistency learning is adopted to leverage unlabeled images using data perturbations. However, the unsupervised outputs may suffer from a lack of shape constraint. Thus, we introduce an adversarial network to enforce the outputs of unlabeled images more reliably. Finally, a global dilated convolution block (GDCB), embedded in U-Net, is designed to obtain global contexts for reducing the effect of artifacts. Extensive experiments are performed on 1400 images of 1259 patients using 1/2, 1/4, and 1/8 labeled training images. Compared to cutting-edge semi-supervised methods, the proposed method can acquire more outstanding results on metrics of DSC and MHD (p value < 0.05). Ablation experiments demonstrate the validity of each proposed module. Besides, plaque clinical parameters are automatedly calculated as a short diagnostic report. Our proposed semi-supervised method can be useful for clinically segmenting carotid ultrasound plaques by using limited labeled images and numerous unlabeled images.