Deep　Neural　Network　(DNN)　stands　out　as　one　widely　adopted　and　effective　technique　for　Click-Through　Rate　(CTR)　prediction　in　live　recommender　systems.　However,　the　prevalent　DNN-based　CTR　methods　exhibit　two　main　drawbacks.　On　one　hand,　they　fail　to　align　their　optimization　objectives　with　the　benchmark　metric,　such　as　the　Area　Under　the　ROC　Curve　(AUC),　designed　for　ranking　tasks.　On　the　other　hand,　current　DNNbased　CTR　solutions　indiscriminately　treat　all　positive-negative　item　pairs,　ignoring　the　fact　that　each　item　pair　differently　contributes　to　AUC　optimization.　To　this　end,　we　propose　Rank　Gap　Sensitive　Deep　AUC　maximization　method　for　accurate　CTR　prediction,　namely　RgsAUC.　Specifically,　we　target　AUC　as　the　learning　objective　by　relaxing　the　Heaviside　function　via　sigmoid　function　to　render　it　differentiable　and　thus　can　be　optimized　directly　using　gradient-descent　methods,　which　is　the　de　facto　choice　for　solving　DNN-based　CTR　tasks.　Furthermore,　we　incorporate　a　rank　gap　sensitive　weight　in　estimating　gradients　for　items,　aiming　to　assign　greater　significance　to　item　pairs　with　substantial　rank　gaps　during　the　learning　process.　In　particular,　we　reduce　the　computational　complexity　from　quadratic　to　linear　through　reformulation,　enabling　efficient　deployment.　Consequently,　these　designs　sharply　minimize　the　number　of　erroneously-ranked　item　pairs,　which　is　beneficial　to　AUC　optimization.　Notably,　RgsAUC　is　model-agnostic　and　we　implement　it　in　five　classic　DNN　models　for　the　CTR　prediction　task.　Extensive　experiments　on　six　real-world　datasets　clearly　demonstrate　the　effectiveness　of　our　proposed　method.