Deep learning has been widely applied in surrogate modeling for airfoil flow field prediction. The success of deep learning relies heavily on large-scale, high-quality labeled samples. However, acquiring labeled samples with complete annotations is prohibitively expensive, and the available annotations in practical engineering are often sparse due to limited observation. To leverage samples with sparse annotations, this paper proposes an uncertainty-based active transfer learning method. The most valuable positions in the flow field are selected based on uncertainty for annotation, effectively improving prediction accuracy and reducing annotation costs. Our method involves a novel active annotation based on synchronous quantile regression, which can mitigate the computational cost of query annotation. Besides, a novel quantile levels-based consistency regularization is proposed to constrain the remaining unlabeled regions and further improve the model performance. Experiments show that our method can significantly reduce prediction errors with only 1% extra annotations, and is a promising tool for achieving rapid and accurate flow field prediction.