Low-light　image　enhancement　is　a　computer　vision　task　that　aims　to　improve　the　visual　perceptual　quality　of　images　captured　in　poorly　illuminated　scenes.　At　present,　deep　learning-based　low-light　enhancement　methods　can　obtain　high-quality　enhanced　images.　However,　it　does　not　consider　the　statistical　characteristics　of　different　regions,　such　as　edge,　structure,　and　texture.　The　uncertainty　of　image　regions　is　not　well　characterized　and　utilized.　To　address　this　problem,　we　propose　a　novel　UnCertainty-driven　Cycle-Consistent　Generative　Adversarial　Network　(UTrCGAN)　to　improve　the　performance　of　low-light　enhancement.　UTrCGAN　first　decomposes　the　unpaired　low/normal-light　images　into　reflectance　and　illumination　components　based　on　the　Retinex　theory.　Then　a　generative　adversarial　network　guided　by　uncertainty　constraint　is　proposed　to　enhance　the　illumination　component,　in　which　the　quality　of　the　enhanced　image　is　further　improved　by　the　guidance　of　variance　estimation.　Experimental　results　on　the　widely-used　LOL　dataset　show　that　UTrCGAN　outperforms　the　state-of-the-art　methods　in　terms　of　visual　quality　and　quantitative　metrics.　©　2024　IEEE.