Low-cost and high-precision infrared meibomian gland segmentation is an important basis for early diagnosis and monitoring of many ocular diseases in ophthalmic clinical practice. To address the issue of limited labeled data, we propose a novel semi-supervised meibomian gland segmentation approach. By leveraging the prior knowledge of the patient each image belongs to, intra-patient registration is taken to generate diverse and lifelike pseudo-labeled data. Contrastive learning strategy with reliable negative sample filtering is also introduced to resolve the insufficient supervision in the feature space. Experimental results on the private dataset demonstrate the success of the proposed approach, exhibiting superiority over the state-of-the-art methods.