Stereo　disparity　estimation　is　a　difficult　and　crucial　task　in　computer　vision.　Although　many　experimental　techniques　have　been　proposed　in　recent　years　with　the　flourishing　of　deep　learning,　very　few　studies　take　into　account　the　optimization　of　computational　complexity　and　memory　consumption.　Most　previous　works　take　advantage　of　stacked　3D　convolutional　block　to　generate　fine　disparity,　but　with　a　high　computational　cost　and　a　large　memory　consumption.　Considering　the　aforementioned　problem,　in　this　paper,　we　proposed　an　efficient　convolutional　neural　architecture　for　stereo　disparity　estimation.　In　particular,　a　compact　and　efficient　multi-scale　extractor　named　MCliqueNet　with　stacked　CliqueBlock　was　proposed　to　extract　the　more　refined　features　for　constructing　multi-scale　cost　volume.　In　order　to　reduce　the　computational　cost　and　maintain　the　accuracy　of　disparity,　we　utilized　knowledge　distillation　scheme　to　transfer　contextual　features　from　a　teacher　network　to　a　student　network.　Furthermore,　we　present　a　novel　adaptive　Smooth(L1)(ASL)　Loss　for　calculating　the　similarity　between　the　contextual　features　of　the　teacher　network　and　those　of　the　student　network,　resulting　in　a　more　robust　distillation　process.　Experimental　results　have　shown　that　our　method　achieves　competitive　performance　on　the　challenging　Scene　Flow　and　KITTI　benchmarks　while　maintaining　a　very　fast　running　speed.