In paired experiments, participants are grouped into pairs with similar characteristics, and one observation from each pair is randomly assigned to treatment. The resulting treatment and control groups should be well-balanced; however, there may still be small chance imbalances. Building on work for completely randomized experiments, we propose a design-based method to adjust for covariate imbalances in paired experiments. We leave out each pair and impute its potential outcomes using any prediction algorithm such as lasso or random forests. This method addresses a unique trade-off that exists for paired experiments. By addressing this trade-off, the method has the potential to improve precision over existing methods.