Mean Absolute Error in Random Forest Regression
Let me clarify few fundamental things:


In sklearn, RandomForrest Regressor criterion is:


  The function to measure the quality of a split


It's a performance measure (by default, MSE) which helps the algorithm to decide on a rule for an optimum split on a node in a tree.
Kaggle is giving you a metric, i.e. MAE (again a performance/ quality measure) but to evaluate the performance of your ML model, once finalized.


To come back to your question: while both MAE/ MSE are performance measures, they are being used at two different stages of a modeling process and might not be related. So, while it makes sense to evaluate your final model on MAE as you would be judged on it, you can choose any of MAE/ MSE for criterion (i.e. for RandomForest) depending on performance at validation stage.

While the above being said, keep in mind that you might want to evaluate the validation errors (i.e. for finalizing a model) on the same metric (i.e. MAE in this case), to keep error measure consistent with the test set evaluation.