Although coronary CT angiography (CCTA) is widely utilized for diagnosing coronary artery disease (CAD), automated CCTA image segmentation is frequently hindered by sparse annotations, pseudo-label noise, and under-delineated fine branches. To mitigate these issues, we present PCDW-Net, a semi-supervised segmentation framework that couples perturbation consistency with discrepancy-aware weighting for enhanced label-scarce performance. Utilizing Adaptive Multi-scale Attention Fusion Network (AMAF-Net) as the backbone within a teacher-student architecture, the network applies diverse perturbations to unlabeled samples, leveraging a consistency loss to promote feature invariance. Simultaneously, a pixel-level discrepancy-aware weighting scheme serves to suppress erroneous pseudo-labels. Evaluated on the public ASOCA and private CTA40 datasets using 10% and 20% annotated fractions, the model was evaluated using Dice similarity coefficient (DSC) and Average Symmetric Surface Distance (ASSD). Under the 20% labeling constraint, PCDW-Net yielded a DSC of 85.36% on ASOCA and 83.48% on CTA40, superior to both Mean Teacher (MT) and Mutual Consistency Network+ (MC-Net+). Ablation studies confirmed the efficacy of each module. Overall, the framework effectively leverages unlabeled volumetric data to yield precise vessel boundary delineations.