Infrared　Small　Target　Detection　is　a　challenging　task　to　separate　small　targets　from　infrared　clutter　background.　Recently,　deep　learning　paradigms　have　achieved　promising　results.　However,　these　data-driven　methods　need　plenty　of　manual　annotations.　Due　to　the　small　size　of　infrared　targets,　manual　annotation　consumes　more　resources　and　restricts　the　development　of　this　field.　This　letter　proposed　a　labor-efficient　annotation　framework　with　level　set,　which　obtains　a　high-quality　pseudo　mask　with　only　one　cursory　click.　A　variational　level　set　formulation　with　an　expectation　difference　energy　functional　is　designed,　in　which　the　zero　level　contour　is　intrinsically　maintained　during　the　level　set　evolution.　It　solves　the　issue　that　zero　level　contour　disappearing　due　to　small　target　size　and　excessive　regularization.　Experiments　on　the　NUAA-SIRST　and　IRSTD-1k　datasets　demonstrate　that　our　approach　achieves　superior　performance.