Semi-supervised　medical　image　segmentation　(SSMIS)　leverages　unlabeled　data　to　reduce　reliance　on　manually　annotated　images.　However,　current　SOTA　approaches　predominantly　focus　on　foreground-oriented　modeling　(i.e.,　segmenting　only　the　foreground　region)　and　have　largely　overlooked　the　potential　benefits　of　explicitly　modeling　the　background　region.　Our　study　theoretically　and　empirically　demonstrates　that　highly　certain　predictions　in　background　modeling　enhance　the　confidence　of　corresponding　foreground　modeling.　Building　on　this　insight,　we　propose　the　Cross-view　Bidirectional　Modeling　(CVBM)　framework,　which　introduces　a　novel　perspective　by　incorporating　background　modeling　to　improve　foreground　modeling　performance.　Within　CVBM,　background　modeling　serves　as　an　auxiliary　perspective,　providing　complementary　supervisory　signals　to　enhance　the　confidence　of　the　foreground　model.　Additionally,　CVBM　introduces　an　innovative　bidirectional　consistency　mechanism,　which　ensures　mutual　alignment　between　foreground　predictions　and　background-guided　predictions.　Extensive　experiments　demonstrate　that　our　approach　achieves　SOTA　performance　on　the　LA,　Pancreas,　ACDC,　and　HRF　datasets.　Notably,　on　the　Pancreas　dataset,　CVBM　outperforms　fully　supervised　methods　(i.e.,　DSC:　84.57%　vs.　83.89%)　while　utilizing　only　20%　of　the　labeled　data.　Our　code　is　publicly　available　at　https://github.com/caoluyang0830/CVBM.git