How to interpret dummy variable in ML prediction?
1) Using drop_first=True is more common in statistics and often referred to as "dummy encoding" while using drop_first=False gives you "one hot-encoding" which is more common in ML. For algorithmic approaches like Random Forests it does not make a difference. Also see "Introduction to Machine Learning with Python"; Mueller, Guido; 2016:


  The one-hot encoding we use is quite similar, but not identical, to
  the dummy encoding used in statistics. For simplicity, we encode
  each category with a different binary feature. In statistics, it is common
  to encode a categorical feature with k different possible values
  into k–1 features (the last one is represented as all zeros). This is
  done to simplify the analysis (more technically, this will avoid making
  the data matrix rank-deficient).


However, using dummy encoding on a binary variable does not mean that a 0 has no relevance. If gender_male has high importance that does not generally say anything about the importance of gender_male==0 vs gender_male==1. It is variable importance and accordingly calculated per variable. If you, for example, use impurity based estimates in Trees it only gives you the average reduction in impurity achieved by splitting on this very variable. 

Moreover, if your gender variable is binary, gender_male==1 is equivalent to gender_female==0. Therefore from a high variable importance of gender_male you cannot infer that being female (or not) is not relevant. 

2) In this case gender_male==0 AND gender_female==0 means Transgender is true. 

3) see 1). For algorithmic approaches in ML there is no statistical disadvantage using one-hot-encoding. (as pointed out in the comments it might even be advantageous since tree-based models can directly split on all features when none is being dropped)