Missing data analyses is complicated.

It all depends on how your data is missing (i.e. missing completely at random, missing at random, missing not at random). How much of the data is missing. The type of data and what you plan to do with the data once imputed?

If the two variables are so highly correlated (assuming this high correlation is not the result of missing data), why can't you use the complete variable instead for your analyses?