Semi-supervised　learning　has　emerged　as　a　critical　approach　for　addressing　medical　image　segmentation　with　limited　annotation,　and　pseudo　labeling-based　methods　made　significant　progress　for　this　task.　However,　the　varying　quality　of　pseudo　labels　poses　a　challenge　to　model　generalization.　In　this　paper,　we　propose　a　Voxel-wise　CLIP-enhanced　model　for　semi-supervised　medical　image　Segmentation　(VCLIPSeg).　Our　model　incorporates　three　modules:　Voxel-Wise　Prompts　Module　(VWPM),　Vision-Text　Consistency　Module　(VTCM),　and　Dynamic　Labeling　Branch　(DLB).　The　VWPM　integrates　CLIP　embeddings　in　a　voxel-wise　manner,　learning　the　semantic　relationships　among　pixels.　The　VTCM　constrains　the　image　prototype　features,　reducing　the　impact　of　noisy　data.　The　DLB　adaptively　generates　pseudo-labels,　effectively　leveraging　the　unlabeled　data.　Experimental　results　on　the　Left　Atrial　(LA)　dataset　and　Pancreas-CT　dataset　demonstrate　the　superiority　of　our　method　over　state-of-the-art　approaches　in　terms　of　the　Dice　score.　For　instance,　it　achieves　a　Dice　score　of　88.51%　using　only　5%　labeled　data　from　the　LA　dataset.