Supervised　learning　algorithms　have　become　the　benchmark　for　medical　image　segmentation　tasks,　but　their　effectiveness　heavily　relies　on　a　large　amount　of　labeled　data　which　is　a　laborious　and　time-consuming　process.　Consequently,　semi-supervised　learning　methods　are　increasingly　becoming　popular.　We　propose　the　Pseudo　Label-Guided　Data　Fusion　framework,　which　builds　upon　the　mean　teacher　network　for　segmenting　medical　images　with　limited　annotation.　We　introduce　a　pseudo-labeling　utilization　scheme,　which　combines　labeled　and　unlabeled　data　to　augment　the　dataset　effectively.　Additionally,　we　enforce　the　consistency　between　different　scales　in　the　decoder　module　of　the　segmentation　network　and　propose　a　loss　function　suitable　for　evaluating　the　consistency.　Moreover,　we　incorporate　a　sharpening　operation　on　the　predicted　results,　further　enhancing　the　accuracy　of　the　segmentation.　Extensive　experiments　on　the　Pancreas-CT,　LA,　BraTS2019　and　BraTS2023　datasets　demonstrate　superior　performance,　with　Dice　scores　of　80.90%,　89.80%,　85.47%　and　89.39%　respectively,　when　10%　of　the　dataset　is　labeled.　Compared　to　MC-Net,　our　method　achieves　improvements　of　10.9%,　0.84%,　5.84%　and　0.63%　on　these　datasets,　respectively.　The　codes　for　this　study　are　available　at　https://github.com/ortonwang/PLGDF.